An Interpretable Automated Machine Learning Credit Risk Model

2020 ◽  
pp. 16-23
Author(s):  
Gabriel Patron ◽  
Diego Leon ◽  
Edwin Lopez ◽  
German Hernandez
Keyword(s):  
Machine Learning ◽  
Credit Risk ◽  
Risk Model ◽  
Automated Machine Learning ◽  
Credit Risk Model

scholarly journals Using Small Business Banking Data for Explainable Credit Risk Scoring

2020 ◽  
Vol 34 (08) ◽  
pp. 13396-13401
Author(s):  
Wei Wang ◽  
Christopher Lesner ◽  
Alexander Ran ◽  
Marko Rukonic ◽  
Jason Xue ◽  
...  
Keyword(s):  
Machine Learning ◽  
Small Business ◽  
Credit Risk ◽  
Risk Model ◽  
Learning Models ◽  
Machine Learning Model ◽  
Risk Scoring ◽  
Financial Transactions ◽  
Credit Risk Model ◽  
Machine Learning Models

Machine learning applied to financial transaction records can predict how likely a small business is to repay a loan. For this purpose we compared a traditional scorecard credit risk model against various machine learning models and found that XGBoost with monotonic constraints outperformed scorecard model by 7% in K-S statistic. To deploy such a machine learning model in production for loan application risk scoring it must comply with lending industry regulations that require lenders to provide understandable and specific reasons for credit decisions. Thus we also developed a loan decision explanation technique based on the ideas of WoE and SHAP. Our research was carried out using a historical dataset of tens of thousands of loans and millions of associated financial transactions. The credit risk scoring model based on XGBoost with monotonic constraints and SHAP explanations described in this paper have been deployed by QuickBooks Capital to assess incoming loan applications since July 2019.

scholarly journals CREDIT RISK MODELLING FOR INDIAN DEBT SECURITIES USING MACHINE LEARNING

2021 ◽  
Vol 24 ◽  
pp. 107-128
Author(s):  
Charumathi Balakrishnan ◽  
Mangaiyarkarasi Thiagarajan
Keyword(s):  
Machine Learning ◽  
Credit Risk ◽  
Credit Rating ◽  
Risk Model ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Rating Agencies ◽  
Learning Techniques ◽  
Classical Models ◽  
Credit Risk Model

We develop a new credit risk model for Indian debt securities rated by major credit rating agencies in India using the ordinal logistic regression (OLR). The robustness of the model is tested by comparing it with classical models available for ratings prediction. We improved the model’s accuracy by using machine learning techniques, such as the artificial neural networks (ANN), support vector machines (SVM) and random forest (RF). We found that the accuracy of our model has improved from 68% using OLR to 82% when using ANN and above 90% when using SVM and RF.

Replication of Contingent Claims in a Reduced-Form Credit Risk Model with Discontinuous Asset Prices

2006 ◽  
Vol 22 (4) ◽  
pp. 661-687 ◽  
Author(s):  
Tomasz R. Bielecki ◽  
Monique Jeanblanc ◽  
Marek Rutkowski
Keyword(s):  
Credit Risk ◽  
Asset Prices ◽  
Risk Model ◽  
Contingent Claims ◽  
Reduced Form ◽  
Credit Risk Model
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